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We developed an Al/O/H ReaxFF force field to explore chemical reactions on α-Al2O3surfaces in H2O/H2gas-phase environments. This force field generates surface energy profiles of A-, C-, R-, and M-planes with various terminations (Al- or O-) and predicts the thermodynamic and kinetic behaviors of hydrolysis on Al-terminated α-Al2O3(0001), consistent with quantum chemical studies. Molecular dynamics (MD) simulations of H2O/α-Al2O3(0001) reveal that water autocatalysis plays a significant role in accelerating H2O dissociations on Al-terminated α-Al2O3(0001). Compared with the 50% Al-terminated surface, the 100% Al-terminated surface becomes more easily hydroxylated at temperatures as low as 350 K, relying more on an OxHyclustering mechanism than complete H2O dissociations, and desorbs significantly more H2O molecules once heated up to 500 K or higher. But heating cannot eliminate surface hydroxyls for either case, and achieving a Gibbsite-like surface by H2O exposure is unlikely. H2O dissociations on α-Al2O3(0001) terminated with randomly distributed surface Al species deviate from 1–2 and 1–4 pathways due to irregular vacancy defects, and a random surface appears to be more reactive to H2O than the ordered one with the same surface Al coverage. Simulations of H2/α-Al2O3suggest that the combination of a dense surface O coverage and a low thermodynamic surface stability leads to elevated H2dissociation kinetics. To accelerate the surface O removals of 100% O-terminated α-Al2O3(0001) in H2gas exposure, we reduced the H–H σ bond energy parameter, equivalent to lowering the H2dissociation barrier by ∼ 19.4 kcal/mol during the simulation. After ∼ 1.5 ns, the surface termination became comparable to the 100% Al-terminated one but retained a small quantity of hydroxyls. This force field reveals how the α-Al2O3crystallographic plane and the surface termination influence the dissociation behaviors of H2O/H2gas molecules and lays the foundation for future force field developments targeted at thin film epitaxy on sapphire.

Innovative design enhancements to microbial fuel cells (MFCs) are pivotal for improving their viability as renewable energy sources. This study introduces a dual approach to electrode modification, integrating polyaniline (PANI) and gold nanoparticles (AuNPs) on carbon felt (CF) anodes coupled with a unique cathode composed of DNA origami and chitosan. The synergistic effect of PANI and AuNPs significantly increases the hydrophilicity and conductivity of the anode, leading to a considerable surge in power density. Concurrently, the biocompatible DNA origami and chitosan-functionalized cathode facilitates efficient electron transfer without relying on conventional catalysts. Together, these modifications yield a 52.42% increase in the maximum power density, illustrating a scalable and economical path to potentiate MFCs. The findings present a promising avenue for advancing MFC technology, emphasizing the potential of nanomaterials in enhancing bioelectrochemical systems. This succinct yet comprehensive strategy marks a step forward in the practical application of MFCs for sustainable energy production.

Polyethylene terephthalate (PET) is one of the most widely produced man-made polymers and is a significant contributor to microplastics pollution. The environmental and human health impacts of microplastics pollution have motivated a concerted effort to develop microbe- and enzyme-based strategies to degrade PET and similar plastics. A PETase derived from the bacteria Ideonella sakaiensiswas previously shown to enzymatically degrade PET, triggering multidisciplinary efforts to improve the robustness and activity of this and other PETases. However, because these enzymes only erode the surface of the insoluble PET substrate, it is difficult to measure standard kinetic parameters, such as kon, koff, and kcat, complicating interpretation of the activity of mutants using traditional enzyme kinetics frameworks. To address this challenge, we developed a single-molecule microscopy assay that quantifies the landing rate and binding duration of quantum dot-labeled PETase enzymes interacting with a surface-immobilized PET film. Wild-type PETase binding durations were well fit by a biexponential with a fast population having a 2.7 s time constant, interpreted as active binding events, and a slow population interpreted as nonspecific binding interactions that last tens of seconds. A previously described hyperactive mutant, S238F/W159H had both a faster apparent on-rate and a slower off-rate than wild-type PETase, potentially explaining its enhanced activity. Because this single-molecule approach provides a more detailed mechanistic picture of PETase enzymatic activity than standard bulk assays, it should aid future efforts to engineer more robust and active PETases to combat global microplastics pollution.

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

ABSTRACTBackgroundThis study aimed to adopt the conventional signal detection methods to explore a new way of risk identification and to mine important drug risks from the perspective of big data based on Zhenjiang Adverse Event Reporting System (ZAERS).Research design and methodsData were extracted from ZAERS database between 2012 and 2022. The risks of all the reported drug event combinations were identified at the preferred term level and the standardized MedDRA query level using disproportionality analysis. Then, we conducted signal assessment according to the descriptions of drug labels.ResultsIn total 41,473 ADE were reported and there were 12 risky signals. Signal assessment indicates the suspected causal associations in clindamycin-taste and smell disorders, valsartan-hepatic enzyme increased and valsartan-edema peripheral; the specific manifestations of allergic reactions triggered by clindamycin, cefotaxime, cefazodime, ShexiangZhuanggu plaster, ShexiangZhuifeng plaster, and Yanhuning need to be refined in drug labels. In addition, the drug labels of NiuHuangShangQing tablet/capsule, Fuyanxiao capsule, and BiYanLing tablet should be improved.ConclusionsIn this study, we attempted a new way to find potential drug risks using small spontaneous reporting data. Our findings also suggested the need for more precise identification of allergic risks and the improvement of traditional Chinese medicine labels.

The tunneling process in water-rich silty fine sand stratum often faces challenges such as arch collapse due to the instability of the initial support arch foot. The present study focuses on the Taoshuping 3# inclined shaft section, modifies the two-side heading method (THM), and introduces the double-heading at bottom method (DBM). Field monitoring and numerical simulations are employed to investigate the formation pressure, deformation evolution, and the advantages of the construction scheme using the loosened zone and stress distribution features. The obtained results show that DBM exhibits a maximu m settlement during arch excavation, constituting approximately 36% of the total settlement, with a total value of 222.21 mm. Furthermore, the plastic zone induced by DBM ranges from 1.23 to 2 times the tunnel diameter, with vertical and horizontal surrounding soil pressures of 140.72 kPa and 46.25 kPa, respectively. DBM is markedly superior to THM. This approach reduces the formation of wedge-shaped shear bodies caused by excessive stress at the sidewalls in THM excavation. Even with tunnel excavation support, the surrounding soil maintains an arch effect, validated through calculations using Protodyakonovco theory and Terzaghi theory, verifying the efficiency of the support structure design.

New particle formation (NPF) in fire smoke is thought to be unlikely due to large condensation and coagulation sinks that scavenge molecular clusters. We analyze aircraft measurements over the Amazon and find that fires significantly enhance NPF and ultrafine particle (UFP < 50 nm diameter) numbers compared to background conditions, contrary to previous understanding. We identify that the nucleation of dimethylamine with sulfuric acid, which is aided by the formation of extremely low volatility organics in biomass-burning smoke, can overcome the large condensation and coagulation sinks and explain aircraft observations. We show that freshly formed clusters rapidly grow to UFP sizes through biomass-burning secondary organic aerosol formation, leading to a 10-fold increase in UFP number concentrations. We find a contrasting effect of UFPs on deep convective clouds compared to the larger particles from primary emissions for the case investigated here. UFPs intensify the deep convective clouds and precipitation due to increased condensational heating, while larger particles delay and reduce precipitation.

Two studies were conducted to investigate the relationship between Early Life Adversity and Malevolent Creativity as well as its underlying mechanism. Study 1 established a moderated mediation model to explore the mediating role of Belief in a Just World (BJW) and the moderating role of Dark Triad. A total of 353 college students (Mage= 21.25, SDage= 2.21, 58.9% women) were recruited to fill out questionnaires. The results showed that Early Life Adversity positively predicted Malevolent Creativity behavior and indirectly predicted Malevolent Creativity behavior through BJW. Dark Triad reduced the effects of Early Life Adversity on BJW and intensified the effects of Early Life Adversity on Malevolent Creativity behavior. Study 2 explored the effect of threatening BJW on Malevolent Creativity potential. A total of 180 participants (Mage= 20.53, SDage= 1.7, 64.4% women) were investigated with questionnaires and a Malevolent Creativity Problem‐Solving Task. Study 2 validated study 1 and illustrated that the Malevolent Creativity potential (including fluency, originality, and malevolence) was enhanced when Belief in a Just World was threatened. However, greater fluency was only observed in men. As a whole, these finding emphasizes the important linking role of BJW between Early Life Adversity and Malevolent Creativity, and Dark Triad and gender influence the expression of Malevolent Creativity as well.

Zinc-ion hybrid supercapacitors (ZIHCs) hold great promise in the realm of renewable energy storage. However, their development is severely hampered by the unsatisfactory practical capacity and poor stability. Herein, we report an effective strategy to improve the electrochemical performance by constructing hollow bimetallic nitride TiVN (H-TiVN) using a sacrificial template method. The optimized H-TiVN//Zn shows a high capacitance of 183.7 F g–1at 0.2 A g–1along with excellent stability (an 84.7% capacitance retention after 20,000 cycles at 5 A g–1). More importantly, the energy density can reach 57.42 Wh kg–1, accompanied by a power density of 249.35 W kg–1. Our work not only establishes H-TiVN as a high-performance electrode material for ZIHCs but also provides a general strategy for improving the electrochemical performance of nanomaterial electrodes.

Pulmonary hypertension (PH) is an intractable, severe, and progressive cardiopulmonary disease. Recent findings suggest that human umbilical cord mesenchymal stromal cells (HUCMSCs) and HUCMSC-derived exosomes (HUCMSC-Exos) possess potential therapeutic value for PH. However, whether they have beneficial effects on hypoxic pulmonary hypertension (HPH) is unclear. Exos are released into the extracellular environment by the fusion of intracellular multivesicular bodies with the cell membrane, and they play an important role in cellular communication. Exos ameliorate immune inflammation levels, alter macrophage phenotypes, regulate mitochondrial metabolic function, and inhibit pulmonary vascular remodeling, thereby improving PH. Macrophages are important sources of cytokines and other transmitters and can promote the release of cytokines, vasoactive molecules, and reactive oxygen species, all of which are associated with pulmonary vascular remodeling. Therefore, the aim of this study was to investigate whether HUCMSC-Exos could improve the lung inflammatory microenvironment and inhibit pulmonary vascular remodeling by targeting macrophages and identifying the underlying mechanisms. The results showed that HUCMSC-Exos promoted M2 macrophage polarization, decreased pro-inflammatory factors, increased IL-10 levels, and inhibited IL-33/ST2 axis expression, thereby inhibiting hypoxia-induced proliferation of pulmonary artery smooth muscle cells and ameliorating HPH.Graphical Abstract

Selective laser melting (SLM) known as a rapid manufacturing technology can fabricate complex and dense metallic components of 18Ni300 with excellent properties. The performance of 18Ni300 can be improved by intermetallics using heat treatment, not including anti-corrosion property. In this study, the effects of heat and coating treatments on microstructure and properties of 18Ni300 fabricated by SLM were investigated. The characterization results indicated that grain coarsening, dislocation reduction, and texture strengthening occurred after the optimal heat treatment. The sequential processing of 820 °C for 2 h and 480 °C for 3 h can maximize the microhardness (increased by 74 %) and ultimate tensile strength (increased by 79 %), accompanying cost of Charpy impact (reduced by 43 %) and elongation (decreased by 32 %). However, excessively high or low aging temperature and time can lead to insufficient performance improvement because of austenite reversal. In addition, the great interface bonding is observed after the laser cladding coating treatment, and the wear resistance maintains well while improving the anti-corrosion property significantly with low quality loss.

Perovskite nanocrystals (PNs) have received much attention as luminescence materials in the field of electrochemiluminescence (ECL). However, as one key factor for determining the optoelectronic properties of the surface state of PNs, the surface passivation layer of PNs has enormous difficulty in simultaneously meeting the requirements of high ECL efficiency, conductivity, and stability. Herein, an effective surface modification strategy with cyanuric acid (CA) is used to solve such issue. As confirmed, the CA molecules are chemically anchored onto the surface of PNs via the Lewis interaction between π electrons of the triazine ring and the empty orbit of Pb2+. Benefiting from the above interaction, the electrochemical impedance of PNs is decreased greatly without the loss of light-emitting efficiency. Moreover, the stability of PNs under O2exposure is improved by almost sixfold. These improvements are confirmed to be beneficial for enhancing the ECL behaviors of PNs under electrochemical operation. Upon cathode ECL driving conditions in aqueous media, the ECL intensity and efficiency of PNs are increased to 200 and 170%, respectively. This work provides a new modification strategy to holistically improve the ECL performance of PNs, which is instructive to exploring robust perovskite nanomaterials for electrochemical applications.

Purpose: Non-invasive fetal electrocardiography (fECG) has a promising application prospect in offering crucial information for assessing early diagnosis and intervention of fetal distress and morbidity during pregnancy. Nevertheless, the detection and extraction of fetal ECG signals are still challenging since fetal ECG signals are exceedingly weak, and liability is affected by maternal ECG and other noises. Methods: In this study, a comprehensive framework is developed for fECG signal extraction and fetal QRS complex location. A negative entropy-based blind source separation (BSS) method combined with a template subtraction (TS) method is exploited to obtain fECG signals from abdominal ECG (aECG) recordings. It effectively combines the arithmetic characteristics of fixed-point iteration and the effectiveness of template filtering, making the algorithm simple and fast to obtain clearer fetal ECG signals. Additionally, the combination of filter transformation and adaptive threshold algorithm is adopted for fetal QRS wave location. The filtering operation makes the fECG signal into single peaks. The design of low threshold and high threshold ensures that R waves can be located and detected more accurately. Results: The performance results in terms of diagnostic sensitivity (Se), positive predictive value (PPV), accuracy (ACC), and harmonic mean (F1) scores are 96.12%, 96.20%, 92.60%, and 95.94% for the PCDB database, respectively, and 99.78%, 99.10%, 98.88%, and 99.44% for the ADFECGDB database. In addition, the results in terms of Se, PPV, ACC, and F1 scores are 99.46%, 97.89%, 97.37%, and 98.67% for the AECGDB database, respectively. Conclusion: This study demonstrates that the proposed framework exhibits superior performance, which can improve the accuracy of fetal QRS complex detection.

Purpose: Non-invasive fetal electrocardiography (fECG) offers crucial information for assessing early diagnosis of fetal distress and morbidity. However, the non-invasive fECG signals probably contain various non-stationary noises, which may generate a bad influence on signal processing. Signal quality assessment plays a crucial role in accurate feature estimation for obtaining high-quality signals. Methods: This study develops a comprehensive framework for the assessment of signal quality for non-invasive fECG signals. Firstly, the ECG collection equipment is employed to gather abdominal ECG signal data from eight pregnant women in the hospital. Secondly, signal preprocessing is operated including signal segmentation and data normalization process. Subsequently, a total of thirty-seven signal quality indexes (SQIs) are extracted which consist of the amplitude-based SQI, R-wave-based SQI, statistical-based SQI, fractal dimension SQI, power spectrum distribution-based SQI, and entropy domain-based SQI. Then, in order to reduce the dimensionality of features and improve the experimental performance, information gain is carried out to identify the subset of the optimal features. At last, the classifier combines different feature numbers to classify the quality of the non-invasive fECG signal. Results: Ten classifiers are selected to perform a classification task between good-quality and bad-quality abdominal signals. The experimental results show that the combination of twenty-four effective features and random forest achieved the highest classification outcome, which in terms of the ACC, and F1 scores are 0.9508, and 0.9510, respectively. Conclusion: The experimental results indicate that our work can reliably assess the signal quality for non-invasive fECG signals and filter out good-quality signals. This proposed algorithm can help to improve the accuracy of fetal signal extraction and fetal heart rate estimation for further analysis, which is beneficial to promoting fetal health monitoring.

LiDAR sensors provide rich geometrical information for 3-D scene understanding, which has been widely used as a unique input for 3-D object detection. However, due to the intrinsic property, point clouds scanned by LiDAR are always sparse and incomplete, and objects are occluded to different extents, which will deteriorate the detection accuracy. The existing methods overlook occlusion or tackle occlusion implicitly. In this article, we emphasize the universality of occlusion in point clouds and propose a novel occlusion–attention strategy, which aims to increase model’s sensitivity to occlusion and maintain great performance in occlude scenes. The proposed method simulates different types and levels of occlusion and explores the relationship between the uncertainty caused by occlusion and the prediction distribution. The major changes include the following: 1) data augmentation specifically for occlusion scenes to force feature extractor into learning efficient features regardless of damage and 2) uncertainty estimation module to model prediction as a distribution instead of the deterministic label. We incorporate the proposed methods into various classical 3-D base detectors and demonstrate performance gain in the KITTI dataset, which proves the particularity of occlusion structure and the necessity of uncertainty estimation.

In recent years, the field of bionics has attracted the attention of numerous scholars. Some models combined with biological vision have achieved excellent performance in computer vision and image processing tasks. In this paper, we propose a new bio-inspired lightweight contour detection network (BLCDNet) by combining parallel processing mechanisms of bio-visual information with convolutional neural networks. The backbone network of BLCDNet simulates the parallel pathways of ganglion cell–lateral geniculate nucleus and primary visual cortex (V1) area, realizing parallel processing and step-by-step extraction of input information, effectively extracting local features and detailed features in images, and thus improving the overall performance of the model. In addition, we design a depth feature extraction module combining depth separable convolution and residual connection in the decoding network to integrate the output of the backbone network, which further improves the performance of the model. We conducted a large number of experiments on BSDS500 and NYUD datasets, and the experimental results show that the BLCDNet proposed in this paper achieves the best performance compared with traditional methods and previous biologically inspired contour detection methods. In addition, BLCDNet still outperforms some VGG-based contour detection methods without pre-training and with fewer parameters, and it is competitive among all of them. The research in this paper also provides a new idea for the combination of biological vision and convolutional neural networks.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

In this brief, the resilient consensus problem for multi-agent systems (MASs) is addressed based on the dynamic event-triggered (DE) mechanism, when the network is subject to malicious attacks. A dynamic variable is introduced in the DE mechanism to adjust the threshold dynamically. Via the proposed dynamic event-triggered mean-subsequence-reduced (DE-MSR) algorithm, all cooperative agents are guaranteed to reach an agreement on the same consensus value, so that the MAS achieves resilient consensus despite the influence of some noncooperative agents in the network. Compared to existing event-based resilient algorithms, the proposed DE-MSR algorithm is superior in reducing communication overheads while maintaining a resilient consensus. Finally, a comparative case study is conducted to validate the theoretical findings.

In this brief, a novel inverse design model, constructed using a generative adversarial network (GAN), is proposed for a varactor-based microstrip tunable lowpass filter (TLPF). Transfer learning algorithm is utilized to simplify the training process of the constructed model. The planar resonant circuits of the TLPFs are represented as pixelated patterns and tunable characteristics are achieved by the tunable capacitance of the varactors. The GAN-based model incorporates multiple simulators that are constructed using convolutional neural networks (CNNs) to predict the outcomes under various tuning conditions. In this way, the relationship between these patterns and the corresponding EM behaviors is established under different tuning voltages applied to the varactors. To significantly reduce the training time and the number of samples required by the extra simulators, an effective solution based on transfer learning is proposed by expanding a single CNN of a well-trained simulator to a series of CNNs of similar simulators with a common architecture. Utilizing the inverse design model, four TLPFs with different customized tuning ranges within 5 to 10 GHz are designed with an efficient process. The training of the model requires approximately 201 min, while the inference time for each design is 12.5 min. The simulation results of the designed TLPFs are in good agreement with the customized ones. Circuit prototypes combining these TLPFs and two additional rejection cells were fabricated and the measurement results show that a 17 dB rejection level is achieved over a wide stopband up to 28 GHz.

Hierarchical frameworks—a special class of directed frameworks with a layer-by-layer architecture—can be an effective mechanism to coordinate robot swarms. Their effectiveness was recently demonstrated by the mergeable nervous systems paradigm (Mathews et al., 2017), in which a robot swarm can switch dynamically between distributed and centralized control depending on the task, using self-organized hierarchical frameworks. New theoretical foundations are required to use this paradigm for formation control of large swarms. In particular, the systematic and mathematically analyzable organization and reorganization of hierarchical frameworks in a robot swarm is still an open problem. Although methods for framework construction and formation maintenance via rigidity theory exist in the literature, they do not address cases of hierarchy in a robot swarm. In this article, we extend bearing rigidity to directed topologies and extend the Henneberg constructions to generate self-organized hierarchical frameworks with bearing rigidity. We investigate three-key self-reconfiguration problems: 1) framework merging; 2) robot departure; and 3) framework splitting. We also derive the mathematical conditions of these problems and then develop algorithms that preserve rigidity and hierarchy using only local information. Our approach can be used for formation control generally, as in principle it can be coupled with any control law that makes use of bearing rigidity. To demonstrate and validate our proposed hierarchical frameworks and methods, we apply them to four scenarios of reactive formation control using an example control law.

Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here, we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size: ⩾ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). The results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

This brief presents a prescribed-time bearing-based formation control scheme for underactuated autonomous surface vessels (ASVs) subject to external disturbances. Compared with the existing methods, our method enables the ASV swarm to maneuver with flexible size scaling and user-defined convergence time. Assuming that the underlying graph of ASV swarm satisfies the leader-first follower structure, a hierarchical formation protocol is designed. We firstly design a prescribed-time disturbance observer to estimate the external disturbance. On this basis, a bearing-based formation control law is integrated such that the vessels achieve the target formation within user-defined time. Rigorous theoretical stability analysis and comparative simulation results are presented to substantiate the effectiveness of the proposed method.

We developed a rotating scanning Hall probe apparatus as a non-destructive measurement method for measuring the self-generated magnetic field distribution around the superconducting cable. To validate the accuracy of our system, we performed measurements by energizing the tape of the outer conductor of the cable, one by one. We confirmed that the measured values are in good accordance with the calculated values. It was also confirmed that the measurement results of the magnetic field distribution when current was passed through two tapes simultaneously coincided well with the sum of the magnetic field distributions when current was passed one by one. In addition, we found that some adjacent tapes within the cable were in contact with each other. These results suggest that this method can also be used as a non-destructive testing method for cable quality control.

This letter proposes a novel wideband crossed-dipole antenna that utilizes liquid metal switches for polarization reconfiguration. The antenna consists of a wideband single-fed crossed bowtie dipole with each arm featuring a gradually varying width bent slot. A back cavity structure serves as a reflector to achieve directional radiation and circular polarization (CP) bandwidth enhancement. The proposed antenna uses movable liquid metal columns in microchannels as coupling switches to open or close the slots. By controlling the switches to close either the vertical or horizontal slots, the antenna achieves directional radiation of left-hand circular polarization or right-hand circular polarization, respectively. When all the slots are opened or closed simultaneously, the directional radiation of linear polarization (LP) is achieved. Experimental results indicate that the antenna has an impedance bandwidth (IBW) of 65.02% and an axial ratio bandwidth of 43.14% in the CP mode, with a maximum gain of 11.6 dBic. In the LP mode, the antenna exhibits an IBW of 42.5% and a maximum gain of 11.48 dBi. These results demonstrate the proposed antenna's versatility and potential for use in mobile communication terminals.

Vaccination is the best way to build an immune barrier to control a new infectious disease. Considering the limited production capacity at the initial stage of vaccine production, the problem of multistage vaccine production and allocation is studied under the policy of free treatment for infected patients by the government. Given the population and infection rates of different regions and the vaccine production capacity of each stage, an integer programming model is established to minimize the sum of production-and-vaccination cost and treatment cost of infected patients, which is solved by the GUROBI solver. The correctness of the model is verified by simulation, and the necessity of considering the treatment cost of infected patients in the objective function is further analyzed, which confirmed the correctness of the free treatment policy for patients in China. A heuristic algorithm is designed to solve the large-scale problem and numerical experiments are conducted to verify the efficiency of this algorithm. Furthermore, based on the sensitivity analysis results of parameters, the optimal vaccine production and allocation strategy are proposed, which provide the decision basis for the government departments to make reasonable vaccine production and vaccination plan.

CD8+invariant natural killer T (iNKT) cells are functionally different from other iNKT cells and are enriched in human but not in mouse. To date, their developmental pathway and molecular basis for fate decision remain unclear. Here, we report enrichment of CD8+iNKT cells in neonatal mice due to their more rapid maturation kinetics than CD8-iNKT cells. Along developmental trajectories, CD8+and CD8-iNKT cells separate at stage 0, following stage 0 double-positive iNKT cells, and differ in HIVEP3 expression. HIVEP3 is lowly expressed in stage 0 CD8+iNKT cells and negatively controls their development, whereas it is highly expressed in stage 0 CD8-iNKT cells and positively controls their development. Despite no effect on IFN-?, HIVEP3 inhibits granzyme B but promotes interleukin-4 production in CD8+iNKT cells. Together, we reveal that, as a negative regulator for CD8+iNKT fate decision, low expression of HIVEP3 in stage 0 CD8+iNKT cells favors their development and T helper 1–biased cytokine responses as well as high cytotoxicity.As a negative regulator for CD8+iNKT fate decision, a low HIVEP3 level in CD8+iNKT cells favors their development, Th1-biased responses, and high cytotoxicity.

Our previous study found that fucogalactan sulfate (FS) from Laminaria japonicaexhibited significant hypolipidemic effects. To further elucidate the mechanism, we first constructed a dyslipidemia mouse model with humanized gut microbiota and proved the main differential metabolic pathway involved bile acid metabolism. Then, we evaluated the beneficial effects of FS on dyslipidemia in this model mice, which revealed that oral FS administration reduced serum cholesterol levels and mitigated liver fat accumulation. Gut microbiota and microbiome analysis showed FS increased the abundance of Ruminococcaceae_NK4A214_group, GCA-900066755, and Eubacterium, which were positively associated with the fecal DCA, β-MCA, and HDCA. Further investigation demonstrated that FS inhibited the hepatic farnesoid X receptor (FXR), while activating the intestinal FXR-FGF19 pathway, leading to suppression of CYP7A1 and CYP8B1, as well as potentially reduced bile acid synthesis and lipid absorption. Overall, FS regulated lipid metabolism in diet-induced humanized dyslipidemia mice via the bile acid-mediated intestinal FXR-FGF19-CYP7A1/CYP8B1 pathway.